Machine Learning Engineer (Energy Analytics & Forecasting)

Diagonally is building intelligent analytics systems for energy and infrastructure operations. Our focus is on predictive analytics, anomaly detection, operational optimization, and AI-assisted infrastructure insights for utility and industrial environments.

We are looking for a Machine Learning Engineer who is comfortable working across forecasting systems, data pipelines, model experimentation, and production-oriented AI workflows.

This role is ideal for someone who enjoys building practical ML systems that solve real operational problems rather than purely academic experimentation.

Responsibilities

Develop machine learning models for:

• time-series forecasting
• anomaly detection
• operational analytics
• predictive maintenance
• infrastructure monitoring
• Work with large structured operational datasets and build reliable preprocessing and feature engineering pipelines
• Design and evaluate forecasting systems using historical utility and operational data
• Build and optimize ETL/data processing workflows

Experiment with ML approaches including:

• XGBoost
• LightGBM
• Random Forest
• deep learning approaches where appropriate
• Collaborate with backend and platform engineers to productionize ML workflows
• Improve model performance, reliability, and scalability
• Assist in defining data architecture and model evaluation strategies

Requirements

• Strong Python Proficiency

Experience with:

• scikit-learn
• pandas
• NumPy
• XGBoost / LightGBM
• PyTorch or TensorFlow

Understanding of:

• time-series forecasting
• anomaly detection
• feature engineering
• model evaluation
• statistical analysis
• Experience building or working with data pipelines and real-world datasets
• Ability to communicate technical decisions clearly
• Comfortable working in a fast-moving startup environment

Nice to Have

Experience with utility, IoT, infrastructure, or operational datasets

Experience with:

• forecasting frameworks
• transformer-based time-series models
• streaming systems
• cloud ML infrastructure

Familiarity with:

• PostgreSQL
• Redis
• Docker
• FastAPI

What We Value

• Strong problem-solving ability
• Ownership and initiative
• Practical engineering mindset
• Curiosity and adaptability
• Ability to balance experimentation with production reliability

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